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Article ; Online: Educators making a difference.

Tehrani, Ali

2020 Volume 51, Issue 1, Page(s) 6

MeSH term(s)	Education, Nursing ; Faculty, Nursing ; Humans ; Mentors ; Professional Role
Language	English
Publishing date	2020-01-13
Publishing country	United States
Document type	Journal Article ; Personal Narrative
ZDB-ID	605889-9
ISSN	1538-8670 ; 0744-6314
ISSN (online)	1538-8670
ISSN	0744-6314
DOI	10.1097/01.NUMA.0000617068.96061.33
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Article ; Online: Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography.

Ashikuzzaman, Md / Tehrani, Ali K Z / Rivaz, Hassan

IEEE transactions on medical imaging

2023 Volume 42, Issue 11, Page(s) 3307–3322

Abstract: Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture ... ...

Abstract	Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1 -MechSOUL ( L1 -norm-based MechSOUL), which optimize L2 - and L1 -norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1 -MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1 -MechSOUL at https://code.sonography.ai.
MeSH term(s)	Elasticity Imaging Techniques/methods ; Algorithms ; Phantoms, Imaging
Language	English
Publishing date	2023-10-27
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2023.3282542
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Article ; Online: Lateral Strain Imaging Using Self-Supervised and Physically Inspired Constraints in Unsupervised Regularized Elastography.

Tehrani, Ali K Z / Ashikuzzaman, Md / Rivaz, Hassan

IEEE transactions on medical imaging

2023 Volume 42, Issue 5, Page(s) 1462–1471

Abstract: Convolutional Neural Networks (CNN) have shown promising results for displacement estimation in UltraSound Elastography (USE). Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction. However, ... ...

Abstract	Convolutional Neural Networks (CNN) have shown promising results for displacement estimation in UltraSound Elastography (USE). Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction. However, the lateral strain, which is essential in several downstream tasks such as the inverse problem of elasticity imaging, remains a challenge. The lateral strain estimation is complicated since the motion and the sampling frequency in this direction are substantially lower than the axial one, and a lack of carrier signal in this direction. In computer vision applications, the axial and the lateral motions are independent. In contrast, the tissue motion pattern in USE is governed by laws of physics which link the axial and lateral displacements. In this paper, inspired by Hooke's law, we, first propose Physically Inspired ConsTraint for Unsupervised Regularized Elastography (PICTURE), where we impose a constraint on the Effective Poisson's ratio (EPR) to improve the lateral strain estimation. In the next step, we propose self-supervised PICTURE (sPICTURE) to further enhance the strain image estimation. Extensive experiments on simulation, experimental phantom and in vivo data demonstrate that the proposed methods estimate accurate axial and lateral strain maps.
MeSH term(s)	Elasticity Imaging Techniques/methods ; Algorithms ; Computer Simulation ; Neural Networks, Computer ; Phantoms, Imaging
Language	English
Publishing date	2023-05-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2022.3230635
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Article ; Online: Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches.

Tehrani, Ali K Z / Cloutier, Guy / Tang, An / Rosado-Mendez, Ivan M / Rivaz, Hassan

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

2024 Volume 71, Issue 3, Page(s) 354–365

Abstract: Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to ...

Abstract	Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to estimate several QUS parameters. Different distributions have been proposed to model envelope data. The homodyned K-distribution (HK-distribution) is one of the most comprehensive distributions that can model US backscattered envelope data under diverse scattering conditions (varying scatterer number density and coherent scattering). The scatterer clustering parameter ( α ) and the ratio of the coherent to diffuse scattering power ( k ) are the parameters of this distribution that have been used extensively for tissue characterization in diagnostic US. The estimation of these two parameters (which we refer to as HK parameters) is done using optimization algorithms in which statistical features such as the envelope point-wise signal-to-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized as input to such algorithms. The optimization methods minimize the difference between features and their theoretical value from the HK model. We propose that the true value of these statistical features is a hyperplane that covers a small portion of the feature space. In this article, we follow two approaches to reduce the effect of sample features' error. We propose a model projection neural network based on denoising autoencoders to project the noisy features into this space based on this assumption. We also investigate if the noise distribution can be learned by the deep estimators. We compare the proposed methods with conventional methods using simulations, an experimental phantom, and data from an in vivo animal model of hepatic steatosis. The network weight and a demo code are available online at ht.tp://code.sonography.ai.
Language	English
Publishing date	2024-02-27
Publishing country	United States
Document type	Journal Article
ISSN	1525-8955
ISSN (online)	1525-8955
DOI	10.1109/TUFFC.2024.3357438
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Article ; Online: SCANED: Siamese collateral assessment network for evaluation of collaterals from ischemic damage.

Aktar, Mumu / Xiao, Yiming / Tehrani, Ali K Z / Tampieri, Donatella / Rivaz, Hassan / Kersten-Oertel, Marta

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

2024 Volume 113, Page(s) 102346

Abstract: This study conducts collateral evaluation from ischemic damage using a deep learning-based Siamese network, addressing the challenges associated with a small and imbalanced dataset. The collateral network provides an alternative oxygen and nutrient ... ...

Abstract	This study conducts collateral evaluation from ischemic damage using a deep learning-based Siamese network, addressing the challenges associated with a small and imbalanced dataset. The collateral network provides an alternative oxygen and nutrient supply pathway in ischemic stroke cases, influencing treatment decisions. Research in this area focuses on automated collateral assessment using deep learning (DL) methods to expedite decision-making processes and enhance accuracy. Our study employed a 3D ResNet-based Siamese network, referred to as SCANED, to classify collaterals as good/intermediate or poor. Utilizing non-contrast computed tomography (NCCT) images, the network automates collateral identification and assessment by analyzing tissue degeneration around the ischemic site. Relevant features from the left/right hemispheres were extracted, and Euclidean Distance (ED) was employed for similarity measurement. Finally, dichotomized classification of good/intermediate or poor collateral is performed by SCANED using an optimal threshold derived from ROC analysis. SCANED provides a sensitivity of 0.88, a specificity of 0.63, and a weighted F1 score of 0.86 in the dichotomized classification.
MeSH term(s)	ROC Curve ; Brain Ischemia/diagnosis ; Deep Learning ; Ischemic Stroke/diagnosis ; Humans
Language	English
Publishing date	2024-02-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	639451-6
ISSN	1879-0771 ; 0895-6111
ISSN (online)	1879-0771
ISSN	0895-6111
DOI	10.1016/j.compmedimag.2024.102346
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Article ; Online: An open-source framework for fast-yet-accurate calculation of quantum mechanical features.

Caldeweyher, Eike / Bauer, Christoph / Tehrani, Ali Soltani

Physical chemistry chemical physics : PCCP

2022 Volume 24, Issue 17, Page(s) 10599–10610

Abstract: We present the open-source ... ...

Abstract	We present the open-source framework
MeSH term(s)	COVID-19 ; Humans ; Machine Learning ; SARS-CoV-2 ; Spike Glycoprotein, Coronavirus
Chemical Substances	Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2
Language	English
Publishing date	2022-05-04
Publishing country	England
Document type	Journal Article
ZDB-ID	1476244-4
ISSN	1463-9084 ; 1463-9076
ISSN (online)	1463-9084
ISSN	1463-9076
DOI	10.1039/d2cp01165d
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Book ; Online: Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography

Tehrani, Ali K. Z. / Rivaz, Hassan

2022

Abstract: Displacement estimation is a critical step of virtually all Ultrasound Elastography (USE) techniques. Two main features make this task unique compared to the general optical flow problem: the high-frequency nature of ultrasound radio-frequency (RF) data ... ...

Abstract	Displacement estimation is a critical step of virtually all Ultrasound Elastography (USE) techniques. Two main features make this task unique compared to the general optical flow problem: the high-frequency nature of ultrasound radio-frequency (RF) data and the governing laws of physics on the displacement field. Recently, the architecture of the optical flow networks has been modified to be able to use RF data. Also, semi-supervised and unsupervised techniques have been employed for USE by considering prior knowledge of displacement continuity in the form of the first- and second-derivative regularizers. Despite these attempts, no work has considered the tissue compression pattern, and displacements in axial and lateral directions have been assumed to be independent. However, tissue motion pattern is governed by laws of physics in USE, rendering the axial and the lateral displacements highly correlated. In this paper, we propose Physically Inspired ConsTraint for Unsupervised Regularized Elastography (PICTURE), where we impose constraints on the Poisson's ratio to improve lateral displacement estimates. Experiments on phantom and in vivo data show that PICTURE substantially improves the quality of the lateral displacement estimation. Comment: Accepted in MICCAI 2022
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Signal Processing
Subject code	004
Publishing date	2022-06-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Infusing known operators in convolutional neural networks for lateral strain imaging in ultrasound elastography

Tehrani, Ali K. Z. / Rivaz, Hassan

2022

Abstract: Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacement in the axial direction) can be estimated by the proposed networks. In ... ...

Abstract	Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacement in the axial direction) can be estimated by the proposed networks. In contrast to axial strain, lateral strain, which is highly required in Poisson's ratio imaging and elasticity reconstruction, has a poor quality. The main causes include low sampling frequency, limited motion, and lack of phase information in the lateral direction. Recently, physically inspired constraint in unsupervised regularized elastography (PICTURE) has been proposed. This method took into account the range of the feasible lateral strain defined by the rules of physics of motion and employed a regularization strategy to improve the lateral strains. Despite the substantial improvement, the regularization was only applied during the training; hence it did not guarantee during the test that the lateral strain is within the feasible range. Furthermore, only the feasible range was employed, other constraints such as incompressibility were not investigated. In this paper, we address these two issues and propose kPICTURE in which two iterative algorithms were infused into the network architecture in the form of known operators to ensure the lateral strain is within the feasible range and impose incompressibility during the test phase. Comment: Accepted in MICCAI 2023
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Machine Learning
Subject code	670
Publishing date	2022-10-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Displacement Estimation in Ultrasound Elastography Using Pyramidal Convolutional Neural Network.

Tehrani, Ali K Z / Rivaz, Hassan

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

2020 Volume 67, Issue 12, Page(s) 2629–2639

Abstract: In this article, two novel deep learning methods are proposed for displacement estimation in ultrasound elastography (USE). Although convolutional neural networks (CNNs) have been very successful for displacement estimation in computer vision, they have ... ...

Abstract	In this article, two novel deep learning methods are proposed for displacement estimation in ultrasound elastography (USE). Although convolutional neural networks (CNNs) have been very successful for displacement estimation in computer vision, they have been rarely used for USE. One of the main limitations is that the radio frequency (RF) ultrasound data, which is crucial for precise displacement estimation, has vastly different frequency characteristics compared with images in computer vision. Top-rank CNN methods used in computer vision applications are mostly based on a multilevel strategy, which estimates finer resolution based on coarser ones. This strategy does not work well for RF data due to its large high-frequency content. To mitigate the problem, we propose modified pyramid warping and cost volume network (MPWC-Net) and RFMPWC-Net, both based on PWC-Net, to exploit information in RF data by employing two different strategies. We obtained promising results using networks trained only on computer vision images. In the next step, we constructed a large ultrasound simulation database and proposed a new loss function to fine-tune the network to improve its performance. The proposed networks and well-known optical flow networks as well as state-of-the-art elastography methods are evaluated using simulation, phantom, and in vivo data. Our two proposed networks substantially outperform current deep learning methods in terms of contrast-to-noise ratio (CNR) and strain ratio (SR). Also, the proposed methods perform similar to the state-of-the-art elastography methods in terms of CNR and have better SR by substantially reducing the underestimation bias.
MeSH term(s)	Deep Learning ; Elasticity Imaging Techniques/methods ; Humans ; Image Processing, Computer-Assisted/methods ; Liver/diagnostic imaging ; Phantoms, Imaging
Language	English
Publishing date	2020-11-24
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1525-8955
ISSN (online)	1525-8955
DOI	10.1109/TUFFC.2020.2973047
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Book ; Online: Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography

Ashikuzzaman, Md / Tehrani, Ali K. Z. / Rivaz, Hassan

2023

Abstract	Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1-MechSOUL (L1-norm-based MechSOUL), which optimize L2- and L1-norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1-MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1-MechSOUL at http://code.sonography.ai. Comment: Link to the Supplemental Video: https://drive.google.com/file/d/1uOmt-T4i9MwR98jUoMsu-eOhQ2mgjrBd/view?usp=sharing
Keywords	Electrical Engineering and Systems Science - Image and Video Processing
Subject code	670
Publishing date	2023-05-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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